Sociodemographic predictors of parenting stress among mothers in socio-economically deprived settings in rural and urban Kenya and Zambia

Parental stress occurs when parenting demands exceed the resources available to cope with parenting. Previous research has identified household wealth, educational level, marital status, age, and number of dependent children as predictors of parental stress. However, limited evidence exists from sub-Saharan Africa. This study investigated the sociodemographic predictors of parenting stress among mothers in Kenya and Zambia. This cross-sectional study utilised baseline secondary data from parenting intervention programs implemented in Kisumu County (rural Kenya), Nairobi County (Urban Kenya), and Chisamba District (rural Zambia). Out of 913 caregivers recruited for the parenting program, 844 with complete data were included in the analysis. The mean age was 1.0 (SD = 0.7) years. Parental stress was measured using the Parental Stress Score (PSS) tool and demographic questionnaires were used to collect demographic information. Mean PSS were compared across study sites, and a multiple linear regression model was used to examine associations between sociodemographic predictors (household income, educational level, marital status, maternal age, child age, and number of children aged < 5 years) and PSS, adjusting for clustering and other predictors. From the results, the mean PSS in rural Kenya was 37.6 [SD = 11.8], in urban Kenya was 48.4 [SD = 4.2], and in rural Zambia was 43.0 [SD = 9.1]. In addition, the significant association between PSS and mothers’ income and educational level was only observed in Kenyan study sites (income: Kenya rural β = -0.40, p < 0.001**; Kenya urban, β = − 0.33, p = .02*; Zambia rural, β = − 0.01, p = 0.7) education: Kenya rural, β = − 0.25, p = .005**; Kenya urban, β = − 0.14, p = 0.07; Zambia rural, β = 0.04, p = 0.3). However, marital status, mother’s age, child’s age, and the number of children below five years were not associated with PSS. The results revealed that mothers’ income and education level were negatively associated with PSS, indicating that higher socioeconomic status can buffer the effects of parental stress. Trial registration Pan African Clinical Trials Registry (https://pactr.samrc.ac.za/) database (ID Number: PACTR20180774832663 Date: 26/July/2018; (ID number: PACTR201905787868050 Date: 06/May/2019.


Methodology Study design
This cross-sectional study utilised baseline secondary data from parenting intervention programs implemented in Kisumu County (rural Kenya), Nairobi County (urban Kenya), and Chisamba District (rural Zambia) 28 .In these three studies, participants were assigned to either the intervention group, which provided a parenting program, or the control group, which received standard care from the Ministry of Health and Education.Participants were drawn from villages/clusters.These clusters were purposively selected to ensure a buffer zone between the intervention and the control arms.In both rural Kenya and rural Zambia study sites, the parenting program intervention was implemented by the Episcopal Relief and Development (ERD) team, together with the Zambia Anglican Council Programmes (ZACOP) in Zambia and with ACK Development Services (ADS) Nyanza in Kenya.This program targeted children below three years of age and focused on enhancing caregivers' parenting skills to promote their children's growth and development 34 .For urban Kenya, the African Population and Health Research Centre (APHRC) in partnership with Val Partners and the Ministry of Health implemented an Early Childhood Development (ECD) mobile phone technology application to help young mothers track and respond to their children's developmental progress in a timely manner.This program aimed to improve caregivers' knowledge, attitudes, and practices on children's growth and development through training, the use of mobile phone technology to track and stimulate their children's growth and development, identification of developmental delays, mentorship visits by the Community Health Volunteer (CHVs), and referral to a health facility to address the identified developmental delays.

Study sites
The study reported in this paper was conducted in both Kenya and Zambia.In Kenya's rural areas, the study was conducted in the Awasi-Onjiko ward, Nyando sub-county, and Kisumu County.Nyando sub-county had a lower uptake of immunisation, at 76.6%, compared to the county average of 82%.The proportion of mothers who attended four ANC visits was also lower in Nyando (48.4%), compared to the county average of 49.7%).In addition, the greater Nyanza region has a higher HIV prevalence rate of 19.3%, which is above the national average of 5.9% 35 .The study site in which the parenting program intervention was implemented was identified by the Ministry of Health (MOH) Health Information System (HIS) as the most vulnerable area in the entire Nyando sub-county.
In Zambia, the parenting program was implemented in the Mwantaya and Chamuka wards of Chisamba District in Central Province.The Chisamba district has a population of 103,983, with a higher HIV prevalence rate than the national average (13.4%) 36.Notably, malnutrition rates were high, with stunted growth in children below five years (42.1%).In addition, Mwantaya Ward has only one health clinic despite being sparsely populated 36 .
In urban Kenya, the study was conducted in the Korogocho ward within the Ruaraka sub-county, Nairobi County.Nairobi County, which has 17 sub-counties, has a total population of approximately 4,004,400 37 .Korogocho is a densely populated ward with 41,946 residents and is characterised by high levels of poverty, high birth rates, and a large proportion of young mothers; 25 per cent of mothers of children under five are aged less than 20 years 38 .Poor feeding and childcare practices have been documented among these mothers and are associated with a high stunting rate (50%) and a high infant mortality rate of 57 deaths per 1000 live births 38 .The Korogocho ward was selected for the implementation of the parenting program because of its poor indicators of maternal and child health outcomes 39 .

Sample size calculation
In rural Kenya, the sample size was calculated based on Hemming and Girling's study 40 with six clusters in each arm and a fixed number of clusters.The number of mothers per arm was 132, implying a total sample size of 264 for the two arms.Due to limited resources, with six clusters in each arm, the intracluster correlation (ICC) was set at 0.02, the effect size at 0.43, and the dropout rate at 10%.A similar sampling calculation was performed for rural Zambia 40 , with an assumed minimum detectable effect size of 0.4 with an ICC of = 0.03.The team also estimated a confidence interval of 95%, a power of 80%, and a dropout rate of 10%.Thus, the total sample size for each arm was n = 255 (510 mothers).However, only 395 mothers (children aged < 18 months or pregnant women in the third trimester) met the inclusion criteria.The inclusion criteria included vulnerability status (for example, health and nutrition indicators, HIV exposure and poverty levels) and long-term residency (more than one year) in the study area.
In the Kenyan urban study, the sample size was calculated using the G*Power program, where the program was set to a one-sided t-test involving the difference between two independent means 40 .Using a priori power analysis, we inputted the values of 0.05 and 0.84 the significance levels and a power of respectively.Additionally, equal-sized sample groups were assumed, meaning that the allocation ratio of N1 (intervention group) to N2 (control group) was one.The calculation produced a sample size of 100 for each group, which allowed for a 10% attrition rate gave a result of 110.A total of 254 households were recruited.

Study sample
Out of 913 caregivers recruited for the parenting program 844 with complete data were included in the analysis.We included mother-infant pairs from the three studies that participated in the baseline (Kenya rural, n = 231; Kenya urban, n = 235; Zambia rural, n = 378) with complete data on the outcome and predictors variables as shown in Fig. 1.Therefore, based on the original studies, assumptions could yield a detectable effect size of 0.4 with an ICC of 0.03, a confidence interval of 95%, and 80% power.

Measures
Parental Stress Score (PSS) was used for the outcome and sociodemographic predictor information was collected using a questionnaire for mothers questionnaires.Trained Research Assistants administered the questionnaire using an interviewer-assisted method, and the average duration for each respondent was estimated to be one hour.The questionnaire covered items on mothers' socio-demographics, caregiving knowledge, attitudes, and practices, PSS, health-seeking behaviours, and the Ages and Stages Questionnaire (ASQ).

PSS outcome measure
This study focused on parental stress levels, measured using the PSS tool to obtain information on parents' feelings and perceptions of their parenting experience.Studies on the validity and reliability of PSS tools have indicated good internal consistency, construct validity, convergent validity, and test-retest reliability 33,[41][42][43][44][45] .Notably, for this study, Cronbach's alpha coefficient for the internal consistency of the items in the PSS was acceptable for all study sites (Kenya rural: 0.80, Kenya urban: 0.74, and Zambia rural: 0.84).The mothers' PSS responses were assigned scores based on a 5-point Likert Scale (1 = Strongly Disagree, 2 = Disagree, 3 = Not Sure, 4 = Agree, 5 = Strongly Agree) (Table 1).To compute the PSS, items 1, 2, 5, 6, 7, 8, 17, and 18 were reversed and scored as follows: (1 = 5) (2 = 4) (3 = 3) (4 = 2) (5 = 1) as shown in Table 1.The item scores were then summed.A low score signifies a low level of stress, and a high score indicates a high level of stress.The overall possible scores on the scale ranged from 18 to 90.

Sociodemographic predictor measures
The predictors were the following sociodemographic characteristics: income, education level, marital status, mother's age, child's age, and number of children aged < 5 years.Information on these were collected via a structured questionnaire.Mothers' incomes are reported in bands (below USD 50, between USD 50 and 100, and above USD 100 per month).Mothers' level of education was categorised as primary, secondary, and postsecondary education, which included college and vocational education.Marital status was categorised as married (living together/cohabiting, legally married) or unmarried (single, divorced, separated, or widowed).The number of children aged < 5 years under care included all those living in the same household, including nonbiological children.

Statistical analysis
Data cleaning and analysis were performed using the R software and R Studio 46 .A multiple linear regression model was used to determine the association between mothers' parenting stress scores and their sociodemographic characteristics, with adjustments for clustering and the study arm (control/intervention) at each study site.In the unadjusted model, the outcome variable was PSS, while the predictor variables were income, education level, marital status, mother's age, child's age, and number of children aged < 5 years.In the adjusted model, we www.nature.com/scientificreports/added covariates (clusters and study arms).The full analysis code is available at the Open Science Framework (https:// osf.io/ yfxjc).

Ethics approval and consent to participate
Permission to use these datasets was granted by the African Population and Health Research Centre (APHRC).
The APHRC obtained ethical approval from the Institutional Review Boards (IRBs) of Kenya and Zambia to conduct these studies at the three study sites.Written informed consent was obtained from the study participants before data were collected.For respondents who could neither read nor write, a thumbprint was used as a signature in the presence of a witness.Consent was obtained at every round of data collection.Consent documents and the questionnaire were translated into Kiswahili (Kenya urban study site), Dholuo (Kenya rural study site), and Nyanja and Tonga (Zambia rural study site).Confidentiality of the data and the participants' privacy were always observed during and after data collection.These rural and urban studies were registered under the trial registration numbers PACTR20180774832663 and PACTR201905787868050, respectively.All methods were performed in accordance with the relevant guidelines and regulations of the Declaration of Helsinki.For example, respect for individuals, the right to make informed decisions, and the recognition of vulnerable groups.

Informed consent process
The data collectors sought informed consent from all study participants before they were interviewed.For those who were not able to read, the information sheet was read to them in their local language, and they were asked to provide a thumbprint to signify their consent.Ethical research committees in both countries approved the use of a thumbprint or signature.The ethical research committees in both countries approved the use of a thumbprint or signature (Amref Health Africa's Ethics and Scientific Review Committee in Kenya and the ERES Converge in Zambia).

Patient and public involvement
To design and implement this study, national and regional stakeholders from the Ministry of Health and Education were involved as part of the study team.Data collectors were also recruited from the study community.
Stakeholders participated in the selection of the study site based on health indicators.

Descriptive statistics
Descriptive statistics for sociodemographic characteristics are provided in Table 2. Mothers had a mean age of approximately 27 years in the two rural populations, whereas the urban population had a slightly higher mean age of approximately 29 years.More than 70% of the mothers reported that they had a marital relationship.Notably, most mothers reported that their educational level was secondary school or lower.Regarding their monthly incomes, most participants from the urban population reported a higher income (above USD 100) than the participants in the rural study sites.Slightly more than 60% of the participants reported that they had one child less than 5 years of age.The results of the unadjusted linear regressions showed that in Kenya's rural study site, the mean PSS of mothers with an income above USD 50 was 0.428 standard deviations lower than that of mothers with a monthly income of less than USD 50 (β = − 0.428 [95% CI − 1.483, − 0.518], p < 0.01***).A similar trend was observed at Kenya's urban study site, with a standard deviation of 0.341 (β = − 0.341 [95% CI − 1.638, − 0.221], p = 0.01*).
When adjusted for clustering and study arm, the results from Kenyan rural sites showed that the mean PSS of mothers with income above USD 50 was 0.403 standard deviation lower than that of mothers with a monthly income less than USD 50 (β = − 0.403 [95% CI − 1.422, − 0.455], p < 0.01***).A similar trend was observed in Kenya's urban study site, with a standard deviation of 0.325 (β = − 0.325 [95% CI − 1.598, − 0.174], p = 0.02*), as shown in Tables 3, 4, and 5. Notably, the income category 'above USD 100' was excluded from Kenyan rural study site modelling, as no participants were in this category.Similarly, the above secondary education was excluded from the Zambia rural study site model for the same reason.
When adjusted for clustering and study arm, the results from Kenyan rural sites showed that the mean PSS of mothers with secondary education was 0.25 a standard deviation lower than that of mothers with primary education (β = − 0.250 [95% CI − 0.838, − 0.667], p = 0.01**).The findings from Kenya's urban and Zambia's rural sites showed no association between education and parental stress (Kenya's urban study site: β = − 0.14 [95% CI − 0.607, 0.030], p = 0.07; Zambia rural: β = − 0.042 [95% CI − 0.167, 0.337], p = 0.3).In addition, the findings on other predictors, such as marital status, mother's age, child age, and number of children aged < 5 years, showed no significant association with PSS across the study sites as shown in Tables 3, 4, and 5.

Discussion
This study aimed to establish predictors of parental stress using baseline datasets from three studies in two African countries (rural Kenya, rural Zambia, and urban Kenya).The results showed that parental stress was associated with at least two factors: the mother's income and educational level.In addition, the mean PSS were slightly above scores observed in a study in a low-resourced setting in South Africa in which the baseline mean PSS for the treatment group was 33.1 (SD = 8.7) and the control group was 33.4 (SD = 8.2).We speculate that the higher PSS in Kenya and Zambia settings could be attributed to a higher proportion of the population living in low socioeconomic status compared to South Africa.In addition, cultural variations within communities could have influenced parenting practices and, thereby, variation in PSS.
Table 3. Beta (B), standard errors (SE), probability values (P) and standardised betas (β) for unadjusted and adjusted models on associations between prenatal demographic characteristics and parental stress Kenya rural study site.*Significant at P > .05.All the adjusted models were controlled for clustering.The Parental Stress Score tool ranges from 18 to 90, with a low score indicating a low parental stress level.Therefore, the B coefficients should be interpreted accordingly.Standardised betas (β) were additionally provided to allow for direct comparisons of effect sizes.*Significant at P < 0.05, **Significant at P < 0.01 and ***Significant at P < 0.001.www.nature.com/scientificreports/Findings on high PSS, especially in the poor urban population, as observed in this study, could illuminate the challenges encountered by women in combining childcare and work in poor urban settings, as reported in other studies 47 .Such scores were not reported in rural Zambia and Kenya because of communal childcare practices.That is, the childcare burden is often spread among extended family members, such as grandparents and other relatives 48 .Mothers can leave their children to their grandparents or other relatives while working and carrying out household chores.However, such practices are uncommon in urban settings, where mothers often take their Table 4. Beta (B), standard errors (SE), probability values (P) and standardised betas (β) for unadjusted and adjusted models on associations between prenatal demographic characteristics and parental stress Kenya urban study site.All the adjusted models were controlled for clustering.The Parental Stress Score tool ranges from 18 to 90, with a low score indicating a low parental stress level.Therefore, the B coefficients should be interpreted accordingly.Standardised betas (β) were additionally provided to allow for direct comparisons of effect sizes.*Significant at P < 0.05, **Significant at P < 0.01 and ***Significant at P < 0.001.www.nature.com/scientificreports/children to childcare facilities when they go to work.Notably, the quality of such childcare services is often poor, which could cause parents to worry about whether their child is receiving the right services 49 .Therefore, we speculate that this could be the reason for the higher parental stress at the Kenyan urban study site.Our findings on the predictors of PSS established that the PSS was associated with the mothers' income in Kenyan study sites.These findings extend those of other studies that have shown associations between household income and mothers' PSS 50 .Such findings were also observed in a study conducted on perceived stress related to COVID-19, in which mothers from low-income households reported more uncertainty about their health than their counterparts with higher incomes 51 .Notably, these studies have been carried out in other contexts that do not represent sub-Saharan Africa.Although the findings of the current study were not consistent across the study sites, it is notable that the mean difference in PSS was consistent across the study sites.Therefore, these findings highlight the need for maternal health and childcare interventions to reduce parental stress levels by improving household incomes and reducing childcare-related expenditures, such as providing quality and affordable childcare services.
An association between PSS and mothers' education levels were also observed, though varied across the study sites.These results were similar to another study of sociodemographic predictors of chronic stress in mothers 22 .To enhance parenting skills and reduce stress, it is suggested that mothers with less formal education could benefit from engaging in parenting programs, which have been shown to positively impact their children's development 52 .Therefore, community parenting programs or social groups could assist parents in sharing their parenting challenges and coming up with potential solutions, thereby reducing the stressors that come with parenting.Surprisingly, the number of children aged < 5 years under care was not associated with parental stress at the three study sites.However, similar findings on parental burnout in SSA have been reported in a study conducted in Somalia among women seeking Maternal and Child Health (MCH) services 53 and in Uganda among parents of primary school children 54 .In addition, the findings on marital status, mother's age, and the age of the youngest child were also surprising.Notably, we could not find any study on sociodemographic predictors of parental stress in the SSA context for comparison with our findings.Therefore, future studies could generate further evidence on the specific sociodemographic predictors of PSS among parents in socially deprived settings.

Study limitations
Despite using three study sites that present diverse, disadvantaged populations in SSA, we could not draw a conclusive link between parental stress and mothers' sociodemographic characteristics.Noting that the population represented in this study was from three distinct studies with different study designs and geographical locations, it might be difficult to separate naturally occurring effects from regional and study-design effects.Another limitation was that the mothers' sociodemographic characteristics were based on self-reporting, which could have introduced reporting biases.In addition, our study focused on mothers' parenting stress.Considering the potential contributions of paternal parenting stress to parenting practices and children's developmental outcomes, future studies could focus on investigating predictors of paternal parenting stress and its effects on parenting practices and children's developmental outcomes.Therefore, these findings are indicative of the association between mothers' socio-demographic characteristics and PSS but do not prove causality.

Conclusions and policy implications
This study aimed to determine the predictors of parental stress among mothers in low-resource SSA settings.The findings indicated that parental stress was associated with at least two factors: the mother's income and educational level.However, PSS was not related to other sociodemographic factors such as marital relationship, age of the child, age of the mother, and number of children aged below five years under their care.If our findings are replicated in the same setting, it will be prudent for interventions to focus on improving maternal mental health through poverty-alleviation-related interventions, such as subsidising childcare services and improving household income in poor urban and rural settings. https://doi.org/10.1038/s41598-024-63980-2www.nature.com/scientificreports/

Figure 1 .
Figure 1.Flow diagram of participants from recruitment to analysis.

5 I
feel close to my child(ren) 6 I enjoy spending time with my child(ren) 7 My child(ren) is an important source of affection for me 8 Having child(ren) gives me a more certain and optimistic view for the future 9 The major source of stress in my life is my child(ren) 10 Having child(ren) leaves little time and flexibility in my life 11 Having child(ren) has been a financial burden 12 It is difficult to balance different responsibilities because of my child(ren) 13 The behaviour of my child(ren) is often embarrassing or stressful to me 14 If I had it to do over again, I might decide not to have child(ren) 15 I feel overwhelmed by the responsibility of being a parent 16 Having child(ren) has meant having too few choices and too little control over my life 17 I am satisfied as a parent 18 I find my child(ren) enjoyable Vol:.(1234567890)Scientific Reports | (2024) 14:13055 | https://doi.org/10.1038/s41598-024-63980-2www.nature.com/scientificreports/The mean PSS were higher at Kenya's urban study site than at the two rural study sites (Kenya rural: 37.6 [SD = 11.8],Kenya urban: 48.4 [SD = 4.2], and Zambia rural: 43.0 [SD = 9.1]), as shown in Fig. 2. Notably, in the Kenya rural study site, children's mean age (in years) at preintervention was 0.8 (SD = 0.7) in the Kenyan rural site, while in the Zambia rural study site, children's mean age at preintervention was 0.9 (SD = 0.6) in the Zambia site and 3.4 (SD = 0.4) in the urban Kenyan population.

Table 1 .
Parental stress score (PSS) tool.The following statements describe feelings and perceptions about the experience of being a parent.Think of each of the items in terms of how your relationship with your child or children typically is.Please indicate the degree to which you agree or disagree with the following items by placing the appropriate number in the space provided.1 = Strongly disagree 2 = Disagree 3 = Undecided 4 = Agree 5 = Strongly agree.There is little or nothing I wouldn't do for my child(ren) if it was necessary 3Caring for my child(ren) sometimes takes more time and energy than I have to give 4 I sometimes worry whether I am doing enough for my child(ren)

Table 2 .
Descriptive statistics on sociodemographic characteristics and mean PSS.We also sought to establish associations between household income, educational level, marital status, maternal age, child age, number of children under five years of age, and mothers' PSS.

Table 5 .
Beta (B), standard errors (SE), probability values (P) and standardised betas (β) for unadjusted and adjusted models on associations between prenatal demographic characteristics and parental stress Zambia rural study site.*Significant at P > 0.05.All the adjusted models were controlled for clustering.The Parental Stress Score tool ranges from 18 to 90, with a low score indicating a low parental stress level.Therefore, the B coefficients should be interpreted accordingly.Standardised betas (β) were additionally provided to allow for direct comparisons of effect sizes.*Significant at P < 0.05, **Significant at P < 0.01 and ***Significant at P < 0.001.